Overview

Dataset statistics

Number of variables13
Number of observations170089
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.9 MiB
Average record size in memory104.0 B

Variable types

Numeric11
Categorical2

Alerts

uri has a high cardinality: 170089 distinct valuesHigh cardinality
energy is highly overall correlated with loudness and 1 other fieldsHigh correlation
loudness is highly overall correlated with energy and 1 other fieldsHigh correlation
acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
uri is uniformly distributedUniform
uri has unique valuesUnique
key has 20373 (12.0%) zerosZeros
instrumentalness has 58312 (34.3%) zerosZeros

Reproduction

Analysis started2023-04-30 18:14:58.602352
Analysis finished2023-04-30 18:15:13.846899
Duration15.24 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

danceability
Real number (ℝ)

Distinct1203
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56977831
Minimum0
Maximum0.991
Zeros97
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-04-30T14:15:13.907899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.261
Q10.458
median0.581
Q30.695
95-th percentile0.8326
Maximum0.991
Range0.991
Interquartile range (IQR)0.237

Descriptive statistics

Standard deviation0.1717207
Coefficient of variation (CV)0.3013816
Kurtosis-0.23993926
Mean0.56977831
Median Absolute Deviation (MAD)0.118
Skewness-0.33197851
Sum96913.023
Variance0.029487998
MonotonicityNot monotonic
2023-04-30T14:15:14.002456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.608 439
 
0.3%
0.573 433
 
0.3%
0.606 428
 
0.3%
0.571 422
 
0.2%
0.603 421
 
0.2%
0.565 420
 
0.2%
0.607 413
 
0.2%
0.564 412
 
0.2%
0.581 411
 
0.2%
0.559 408
 
0.2%
Other values (1193) 165882
97.5%
ValueCountFrequency (%)
0 97
0.1%
0.0567 1
 
< 0.1%
0.0568 2
 
< 0.1%
0.0573 2
 
< 0.1%
0.0591 1
 
< 0.1%
0.0596 1
 
< 0.1%
0.0598 1
 
< 0.1%
0.0599 1
 
< 0.1%
0.0601 1
 
< 0.1%
0.0602 1
 
< 0.1%
ValueCountFrequency (%)
0.991 1
 
< 0.1%
0.988 1
 
< 0.1%
0.986 1
 
< 0.1%
0.985 1
 
< 0.1%
0.984 4
< 0.1%
0.983 2
< 0.1%
0.982 4
< 0.1%
0.981 3
< 0.1%
0.98 4
< 0.1%
0.979 2
< 0.1%

energy
Real number (ℝ)

Distinct2124
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62201827
Minimum0
Maximum1
Zeros21
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-04-30T14:15:14.099977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.158
Q10.459
median0.66
Q30.82
95-th percentile0.949
Maximum1
Range1
Interquartile range (IQR)0.361

Descriptive statistics

Standard deviation0.24179157
Coefficient of variation (CV)0.38872102
Kurtosis-0.53050464
Mean0.62201827
Median Absolute Deviation (MAD)0.176
Skewness-0.5505159
Sum105798.47
Variance0.058463166
MonotonicityNot monotonic
2023-04-30T14:15:14.204509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.869 321
 
0.2%
0.794 318
 
0.2%
0.716 318
 
0.2%
0.855 317
 
0.2%
0.728 315
 
0.2%
0.721 315
 
0.2%
0.727 313
 
0.2%
0.837 308
 
0.2%
0.867 306
 
0.2%
0.717 306
 
0.2%
Other values (2114) 166952
98.2%
ValueCountFrequency (%)
0 21
< 0.1%
2 × 10-51
 
< 0.1%
2.02 × 10-52
 
< 0.1%
2.03 × 10-51
 
< 0.1%
2.05 × 10-51
 
< 0.1%
4.87 × 10-51
 
< 0.1%
5.62 × 10-51
 
< 0.1%
6.9 × 10-51
 
< 0.1%
7.08 × 10-51
 
< 0.1%
9.84 × 10-51
 
< 0.1%
ValueCountFrequency (%)
1 8
 
< 0.1%
0.999 19
 
< 0.1%
0.998 60
< 0.1%
0.997 57
< 0.1%
0.996 84
< 0.1%
0.995 101
0.1%
0.994 112
0.1%
0.993 135
0.1%
0.992 102
0.1%
0.991 119
0.1%

key
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2605812
Minimum0
Maximum11
Zeros20373
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-04-30T14:15:14.293999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.5777138
Coefficient of variation (CV)0.68009857
Kurtosis-1.295705
Mean5.2605812
Median Absolute Deviation (MAD)3
Skewness0.0018090546
Sum894767
Variance12.800036
MonotonicityNot monotonic
2023-04-30T14:15:14.361550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 20373
12.0%
7 19728
11.6%
2 17529
10.3%
9 17213
10.1%
1 16449
9.7%
5 13840
8.1%
11 13463
7.9%
4 12813
7.5%
6 11230
6.6%
8 11033
6.5%
Other values (2) 16418
9.7%
ValueCountFrequency (%)
0 20373
12.0%
1 16449
9.7%
2 17529
10.3%
3 5446
 
3.2%
4 12813
7.5%
5 13840
8.1%
6 11230
6.6%
7 19728
11.6%
8 11033
6.5%
9 17213
10.1%
ValueCountFrequency (%)
11 13463
7.9%
10 10972
6.5%
9 17213
10.1%
8 11033
6.5%
7 19728
11.6%
6 11230
6.6%
5 13840
8.1%
4 12813
7.5%
3 5446
 
3.2%
2 17529
10.3%

loudness
Real number (ℝ)

Distinct21575
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-8.2187895
Minimum-60
Maximum2.766
Zeros0
Zeros (%)0.0%
Negative170030
Negative (%)> 99.9%
Memory size1.3 MiB
2023-04-30T14:15:14.450074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-60
5-th percentile-17.101
Q1-9.955
median-7.064
Q3-5.186
95-th percentile-3.266
Maximum2.766
Range62.766
Interquartile range (IQR)4.769

Descriptive statistics

Standard deviation4.6157496
Coefficient of variation (CV)-0.56160942
Kurtosis6.565149
Mean-8.2187895
Median Absolute Deviation (MAD)2.211
Skewness-1.9799435
Sum-1397925.7
Variance21.305144
MonotonicityNot monotonic
2023-04-30T14:15:14.534162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.682 40
 
< 0.1%
-5.385 40
 
< 0.1%
-5.772 39
 
< 0.1%
-5.603 39
 
< 0.1%
-4.683 38
 
< 0.1%
-5.804 38
 
< 0.1%
-5.296 38
 
< 0.1%
-5.526 38
 
< 0.1%
-6.324 38
 
< 0.1%
-6.018 37
 
< 0.1%
Other values (21565) 169704
99.8%
ValueCountFrequency (%)
-60 12
< 0.1%
-47.885 1
 
< 0.1%
-47.669 1
 
< 0.1%
-47.042 1
 
< 0.1%
-46.839 1
 
< 0.1%
-45.545 1
 
< 0.1%
-44.749 1
 
< 0.1%
-44.556 1
 
< 0.1%
-44.15 1
 
< 0.1%
-43.886 1
 
< 0.1%
ValueCountFrequency (%)
2.766 1
< 0.1%
2.745 1
< 0.1%
2.625 1
< 0.1%
2.594 1
< 0.1%
2.574 1
< 0.1%
2.353 1
< 0.1%
2.238 1
< 0.1%
2.172 1
< 0.1%
2.127 1
< 0.1%
2 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
112556 
0
57533 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters170089
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 112556
66.2%
0 57533
33.8%

Length

2023-04-30T14:15:14.624682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-30T14:15:14.711011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 112556
66.2%
0 57533
33.8%

Most occurring characters

ValueCountFrequency (%)
1 112556
66.2%
0 57533
33.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 170089
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 112556
66.2%
0 57533
33.8%

Most occurring scripts

ValueCountFrequency (%)
Common 170089
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 112556
66.2%
0 57533
33.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 170089
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 112556
66.2%
0 57533
33.8%

speechiness
Real number (ℝ)

Distinct1563
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.087511503
Minimum0
Maximum0.963
Zeros97
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-04-30T14:15:14.792546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0279
Q10.0348
median0.0473
Q30.0889
95-th percentile0.306
Maximum0.963
Range0.963
Interquartile range (IQR)0.0541

Descriptive statistics

Standard deviation0.099464025
Coefficient of variation (CV)1.1365823
Kurtosis14.489718
Mean0.087511503
Median Absolute Deviation (MAD)0.016
Skewness3.2024646
Sum14884.744
Variance0.0098930923
MonotonicityNot monotonic
2023-04-30T14:15:14.896609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0328 561
 
0.3%
0.032 558
 
0.3%
0.0332 546
 
0.3%
0.031 545
 
0.3%
0.0326 544
 
0.3%
0.0312 538
 
0.3%
0.0316 533
 
0.3%
0.0308 531
 
0.3%
0.0322 529
 
0.3%
0.0318 528
 
0.3%
Other values (1553) 164676
96.8%
ValueCountFrequency (%)
0 97
0.1%
0.0219 1
 
< 0.1%
0.0222 2
 
< 0.1%
0.0223 2
 
< 0.1%
0.0224 6
 
< 0.1%
0.0225 7
 
< 0.1%
0.0226 6
 
< 0.1%
0.0227 8
 
< 0.1%
0.0228 15
 
< 0.1%
0.0229 8
 
< 0.1%
ValueCountFrequency (%)
0.963 1
 
< 0.1%
0.962 3
< 0.1%
0.961 3
< 0.1%
0.96 2
 
< 0.1%
0.959 1
 
< 0.1%
0.958 3
< 0.1%
0.956 2
 
< 0.1%
0.955 2
 
< 0.1%
0.954 6
< 0.1%
0.953 4
< 0.1%

acousticness
Real number (ℝ)

Distinct4910
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28731746
Minimum0
Maximum0.996
Zeros25
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-04-30T14:15:15.027815image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.000384
Q10.019
median0.141
Q30.513
95-th percentile0.931
Maximum0.996
Range0.996
Interquartile range (IQR)0.494

Descriptive statistics

Standard deviation0.31830089
Coefficient of variation (CV)1.1078369
Kurtosis-0.59816749
Mean0.28731746
Median Absolute Deviation (MAD)0.1381
Skewness0.90638062
Sum48869.539
Variance0.10131546
MonotonicityNot monotonic
2023-04-30T14:15:15.133350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995 311
 
0.2%
0.994 293
 
0.2%
0.993 276
 
0.2%
0.103 267
 
0.2%
0.101 256
 
0.2%
0.11 253
 
0.1%
0.114 252
 
0.1%
0.102 249
 
0.1%
0.119 248
 
0.1%
0.111 245
 
0.1%
Other values (4900) 167439
98.4%
ValueCountFrequency (%)
0 25
< 0.1%
1.07 × 10-62
 
< 0.1%
1.08 × 10-61
 
< 0.1%
1.15 × 10-61
 
< 0.1%
1.2 × 10-61
 
< 0.1%
1.23 × 10-61
 
< 0.1%
1.25 × 10-61
 
< 0.1%
1.27 × 10-61
 
< 0.1%
1.28 × 10-61
 
< 0.1%
1.3 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.996 85
 
< 0.1%
0.995 311
0.2%
0.994 293
0.2%
0.993 276
0.2%
0.992 236
0.1%
0.991 235
0.1%
0.99 186
0.1%
0.989 167
0.1%
0.988 185
0.1%
0.987 180
0.1%

instrumentalness
Real number (ℝ)

Distinct5399
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12418812
Minimum0
Maximum0.999
Zeros58312
Zeros (%)34.3%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-04-30T14:15:15.240860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.73 × 10-5
Q30.0203
95-th percentile0.876
Maximum0.999
Range0.999
Interquartile range (IQR)0.0203

Descriptive statistics

Standard deviation0.2749173
Coefficient of variation (CV)2.2137165
Kurtosis2.826495
Mean0.12418812
Median Absolute Deviation (MAD)3.73 × 10-5
Skewness2.1079729
Sum21123.034
Variance0.075579519
MonotonicityNot monotonic
2023-04-30T14:15:15.342383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 58312
34.3%
0.926 134
 
0.1%
0.911 132
 
0.1%
0.899 124
 
0.1%
0.906 124
 
0.1%
0.912 121
 
0.1%
0.905 120
 
0.1%
0.917 119
 
0.1%
0.91 117
 
0.1%
0.89 115
 
0.1%
Other values (5389) 110671
65.1%
ValueCountFrequency (%)
0 58312
34.3%
1 × 10-627
 
< 0.1%
1.01 × 10-659
 
< 0.1%
1.02 × 10-674
 
< 0.1%
1.03 × 10-675
 
< 0.1%
1.04 × 10-670
 
< 0.1%
1.05 × 10-671
 
< 0.1%
1.06 × 10-671
 
< 0.1%
1.07 × 10-662
 
< 0.1%
1.08 × 10-665
 
< 0.1%
ValueCountFrequency (%)
0.999 2
 
< 0.1%
0.998 2
 
< 0.1%
0.997 1
 
< 0.1%
0.996 1
 
< 0.1%
0.995 1
 
< 0.1%
0.994 5
< 0.1%
0.993 8
< 0.1%
0.992 4
< 0.1%
0.991 1
 
< 0.1%
0.99 2
 
< 0.1%

liveness
Real number (ℝ)

Distinct1749
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20091219
Minimum0
Maximum1
Zeros41
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-04-30T14:15:15.438921image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.05994
Q10.096
median0.127
Q30.255
95-th percentile0.608
Maximum1
Range1
Interquartile range (IQR)0.159

Descriptive statistics

Standard deviation0.17528637
Coefficient of variation (CV)0.87245266
Kurtosis5.2629844
Mean0.20091219
Median Absolute Deviation (MAD)0.0469
Skewness2.2168695
Sum34172.953
Variance0.030725312
MonotonicityNot monotonic
2023-04-30T14:15:15.707753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111 2023
 
1.2%
0.11 1856
 
1.1%
0.108 1760
 
1.0%
0.109 1710
 
1.0%
0.107 1707
 
1.0%
0.105 1649
 
1.0%
0.106 1638
 
1.0%
0.112 1610
 
0.9%
0.103 1609
 
0.9%
0.104 1571
 
0.9%
Other values (1739) 152956
89.9%
ValueCountFrequency (%)
0 41
< 0.1%
0.00673 1
 
< 0.1%
0.00936 1
 
< 0.1%
0.0102 1
 
< 0.1%
0.0109 1
 
< 0.1%
0.0116 1
 
< 0.1%
0.0118 1
 
< 0.1%
0.0119 1
 
< 0.1%
0.012 1
 
< 0.1%
0.0121 2
 
< 0.1%
ValueCountFrequency (%)
1 3
 
< 0.1%
0.999 2
 
< 0.1%
0.998 5
< 0.1%
0.997 3
 
< 0.1%
0.996 7
< 0.1%
0.995 7
< 0.1%
0.994 5
< 0.1%
0.993 10
< 0.1%
0.992 12
< 0.1%
0.991 11
< 0.1%

valence
Real number (ℝ)

Distinct1680
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47289893
Minimum0
Maximum1
Zeros111
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-04-30T14:15:15.802290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0798
Q10.265
median0.46
Q30.674
95-th percentile0.906
Maximum1
Range1
Interquartile range (IQR)0.409

Descriptive statistics

Standard deviation0.25366017
Coefficient of variation (CV)0.53639403
Kurtosis-0.98871747
Mean0.47289893
Median Absolute Deviation (MAD)0.204
Skewness0.15131354
Sum80434.907
Variance0.06434348
MonotonicityNot monotonic
2023-04-30T14:15:15.905719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 415
 
0.2%
0.962 321
 
0.2%
0.963 319
 
0.2%
0.964 289
 
0.2%
0.357 271
 
0.2%
0.96 269
 
0.2%
0.336 266
 
0.2%
0.33 264
 
0.2%
0.524 262
 
0.2%
0.398 258
 
0.2%
Other values (1670) 167155
98.3%
ValueCountFrequency (%)
0 111
0.1%
1 × 10-514
 
< 0.1%
0.00122 1
 
< 0.1%
0.00392 1
 
< 0.1%
0.00485 1
 
< 0.1%
0.00553 1
 
< 0.1%
0.00602 1
 
< 0.1%
0.00847 1
 
< 0.1%
0.00984 1
 
< 0.1%
0.0115 1
 
< 0.1%
ValueCountFrequency (%)
1 4
< 0.1%
0.998 1
 
< 0.1%
0.997 1
 
< 0.1%
0.995 3
< 0.1%
0.993 1
 
< 0.1%
0.992 1
 
< 0.1%
0.991 2
 
< 0.1%
0.99 4
< 0.1%
0.989 2
 
< 0.1%
0.988 6
< 0.1%

tempo
Real number (ℝ)

Distinct71241
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.14205
Minimum0
Maximum235.781
Zeros97
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-04-30T14:15:16.005245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile77.691
Q198.007
median120.133
Q3139.948
95-th percentile174.2446
Maximum235.781
Range235.781
Interquartile range (IQR)41.941

Descriptive statistics

Standard deviation29.39296
Coefficient of variation (CV)0.24263218
Kurtosis-0.17777247
Mean121.14205
Median Absolute Deviation (MAD)20.155
Skewness0.32054288
Sum20604930
Variance863.94607
MonotonicityNot monotonic
2023-04-30T14:15:16.103476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 97
 
0.1%
128.001 76
 
< 0.1%
127.998 70
 
< 0.1%
128.013 69
 
< 0.1%
127.997 67
 
< 0.1%
128 66
 
< 0.1%
127.999 64
 
< 0.1%
127.992 64
 
< 0.1%
128.002 62
 
< 0.1%
119.994 61
 
< 0.1%
Other values (71231) 169393
99.6%
ValueCountFrequency (%)
0 97
0.1%
31.167 1
 
< 0.1%
31.988 1
 
< 0.1%
32.055 1
 
< 0.1%
32.724 1
 
< 0.1%
34.082 1
 
< 0.1%
34.782 1
 
< 0.1%
34.821 1
 
< 0.1%
35.204 1
 
< 0.1%
35.37 1
 
< 0.1%
ValueCountFrequency (%)
235.781 1
< 0.1%
232.08 1
< 0.1%
226.705 1
< 0.1%
220.251 1
< 0.1%
220.169 1
< 0.1%
220.16 1
< 0.1%
220.092 1
< 0.1%
219.918 1
< 0.1%
219.785 1
< 0.1%
219.364 1
< 0.1%

uri
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct170089
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
spotify:track:0UaMYEvWZi0ZqiDOoHU3YI
 
1
spotify:track:7hU88dWKOrWmfLk0HhQvSV
 
1
spotify:track:1ABGVrP1QEzjMPfzM0KXKt
 
1
spotify:track:7jJQzk7Ng6wjc8jk04xXNF
 
1
spotify:track:5XagNVHf4diy4zeM5p0VZc
 
1
Other values (170084)
170084 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters6123204
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique170089 ?
Unique (%)100.0%

Sample

1st rowspotify:track:0UaMYEvWZi0ZqiDOoHU3YI
2nd rowspotify:track:6I9VzXrHxO9rA9A5euc8Ak
3rd rowspotify:track:0WqIKmW4BTrj3eJFmnCKMv
4th rowspotify:track:1AWQoqb9bSvzTjaLralEkT
5th rowspotify:track:1lzr43nnXAijIGYnCT8M8H

Common Values

ValueCountFrequency (%)
spotify:track:0UaMYEvWZi0ZqiDOoHU3YI 1
 
< 0.1%
spotify:track:7hU88dWKOrWmfLk0HhQvSV 1
 
< 0.1%
spotify:track:1ABGVrP1QEzjMPfzM0KXKt 1
 
< 0.1%
spotify:track:7jJQzk7Ng6wjc8jk04xXNF 1
 
< 0.1%
spotify:track:5XagNVHf4diy4zeM5p0VZc 1
 
< 0.1%
spotify:track:2qigyAuvoKtaZZKsnynMFT 1
 
< 0.1%
spotify:track:4e3p8QsYgeMf8eOUwZEIOx 1
 
< 0.1%
spotify:track:1x4AHWj90fpKObG5URZr9g 1
 
< 0.1%
spotify:track:6QFgpUiLoXlmYDjbe0tjyP 1
 
< 0.1%
spotify:track:72UnWvJ8X67nAzjaHNamYr 1
 
< 0.1%
Other values (170079) 170079
> 99.9%

Length

2023-04-30T14:15:16.193983image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
spotify:track:0uamyevwzi0zqidoohu3yi 1
 
< 0.1%
spotify:track:12qzhaeoytf93yawvgdtat 1
 
< 0.1%
spotify:track:215jyyyunrj98nk3kewu6d 1
 
< 0.1%
spotify:track:4bp3uh0hflfrb5cjsglqdh 1
 
< 0.1%
spotify:track:4pmc2axseq6g7hpvljcpyp 1
 
< 0.1%
spotify:track:0wqikmw4btrj3ejfmnckmv 1
 
< 0.1%
spotify:track:1awqoqb9bsvztjalralekt 1
 
< 0.1%
spotify:track:1lzr43nnxaijigynct8m8h 1
 
< 0.1%
spotify:track:0xufyu2qvipas6bxspxyg4 1
 
< 0.1%
spotify:track:68vgtrhr7izhpzgpon6jlo 1
 
< 0.1%
Other values (170079) 170079
> 99.9%

Most occurring characters

ValueCountFrequency (%)
t 397686
 
6.5%
: 340178
 
5.6%
p 228160
 
3.7%
r 227844
 
3.7%
f 227806
 
3.7%
c 227786
 
3.7%
i 227765
 
3.7%
y 227759
 
3.7%
a 227741
 
3.7%
k 227623
 
3.7%
Other values (53) 3562856
58.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3539883
57.8%
Uppercase Letter 1496102
24.4%
Decimal Number 747041
 
12.2%
Other Punctuation 340178
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 397686
 
11.2%
p 228160
 
6.4%
r 227844
 
6.4%
f 227806
 
6.4%
c 227786
 
6.4%
i 227765
 
6.4%
y 227759
 
6.4%
a 227741
 
6.4%
k 227623
 
6.4%
s 227541
 
6.4%
Other values (16) 1092172
30.9%
Uppercase Letter
ValueCountFrequency (%)
F 58043
 
3.9%
D 58019
 
3.9%
I 57910
 
3.9%
B 57882
 
3.9%
E 57799
 
3.9%
L 57754
 
3.9%
A 57717
 
3.9%
Q 57709
 
3.9%
J 57689
 
3.9%
S 57665
 
3.9%
Other values (16) 917915
61.4%
Decimal Number
ValueCountFrequency (%)
0 79780
10.7%
6 79735
10.7%
1 79703
10.7%
5 79545
10.6%
4 79545
10.6%
3 79365
10.6%
2 79161
10.6%
7 74927
10.0%
9 57682
7.7%
8 57598
7.7%
Other Punctuation
ValueCountFrequency (%)
: 340178
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5035985
82.2%
Common 1087219
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 397686
 
7.9%
p 228160
 
4.5%
r 227844
 
4.5%
f 227806
 
4.5%
c 227786
 
4.5%
i 227765
 
4.5%
y 227759
 
4.5%
a 227741
 
4.5%
k 227623
 
4.5%
s 227541
 
4.5%
Other values (42) 2588274
51.4%
Common
ValueCountFrequency (%)
: 340178
31.3%
0 79780
 
7.3%
6 79735
 
7.3%
1 79703
 
7.3%
5 79545
 
7.3%
4 79545
 
7.3%
3 79365
 
7.3%
2 79161
 
7.3%
7 74927
 
6.9%
9 57682
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6123204
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 397686
 
6.5%
: 340178
 
5.6%
p 228160
 
3.7%
r 227844
 
3.7%
f 227806
 
3.7%
c 227786
 
3.7%
i 227765
 
3.7%
y 227759
 
3.7%
a 227741
 
3.7%
k 227623
 
3.7%
Other values (53) 3562856
58.2%

duration_ms
Real number (ℝ)

Distinct65472
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean238712.33
Minimum3056
Maximum5279768
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 MiB
2023-04-30T14:15:16.280497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3056
5-th percentile136000
Q1192867
median225432
Q3266840
95-th percentile376320
Maximum5279768
Range5276712
Interquartile range (IQR)73973

Descriptive statistics

Standard deviation100754
Coefficient of variation (CV)0.42207288
Kurtosis381.35906
Mean238712.33
Median Absolute Deviation (MAD)36139
Skewness11.827945
Sum4.0602341 × 1010
Variance1.0151368 × 1010
MonotonicityNot monotonic
2023-04-30T14:15:16.381005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240000 138
 
0.1%
192000 88
 
0.1%
180000 85
 
< 0.1%
210000 70
 
< 0.1%
216000 70
 
< 0.1%
204000 64
 
< 0.1%
228000 58
 
< 0.1%
200000 57
 
< 0.1%
220000 56
 
< 0.1%
270000 54
 
< 0.1%
Other values (65462) 169349
99.6%
ValueCountFrequency (%)
3056 9
< 0.1%
4000 1
 
< 0.1%
4107 1
 
< 0.1%
4227 1
 
< 0.1%
4520 1
 
< 0.1%
4800 1
 
< 0.1%
4876 1
 
< 0.1%
5227 1
 
< 0.1%
5471 1
 
< 0.1%
5887 1
 
< 0.1%
ValueCountFrequency (%)
5279768 1
< 0.1%
5062000 1
< 0.1%
4788939 1
< 0.1%
4556388 1
< 0.1%
4500037 1
< 0.1%
4497994 1
< 0.1%
4436000 1
< 0.1%
4316155 1
< 0.1%
4276000 1
< 0.1%
4120258 1
< 0.1%

Interactions

2023-04-30T14:15:12.216508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:02.789566image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:03.692396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:04.691831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:05.598297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:06.529780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:07.548674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:08.457015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:09.351904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:10.235509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:11.258848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:12.322025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:02.871049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:03.776111image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:04.773533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:05.688945image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:06.610362image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:07.634691image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:08.537017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:09.428811image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:10.314163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:11.339928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:12.431548image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:02.955466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:03.856619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:04.853344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:05.769656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:06.692002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:07.714746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:08.619544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:09.506933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:10.396901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:11.421435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:12.526666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:03.036001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:03.936173image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:04.929494image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:05.852242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:06.769500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:07.796263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:08.701058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:09.589453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:10.475429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:11.514981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:12.621777image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:03.120002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:04.022163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:05.019919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:05.939778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:06.857916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:07.879907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:08.782570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:09.672253image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:10.558136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:11.603599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:12.712322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:03.201949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:04.105461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:05.101419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:06.028789image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:06.938930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:07.964332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:08.865094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:09.754131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:10.640138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:11.689135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:12.803880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:03.286483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:04.191044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:05.184123image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:06.114316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:07.021141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:08.052845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:08.946697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:09.833193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:10.720643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:11.772276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:12.910447image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:03.368004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:04.270787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:05.264627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:06.200104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:07.101670image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:08.135861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:09.027749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:09.914697image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:10.798169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:11.853347image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:13.010957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:03.450517image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:04.449485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:05.348269image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:06.282674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:07.184193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:08.216862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:09.115256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:09.995231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:10.878583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:11.940363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:13.118465image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:03.531613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:04.530503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:05.429285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:06.365171image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:07.265728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:08.296963image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:09.193601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:10.076859image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:10.959121image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:12.023929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:13.207994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:03.609855image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:04.610023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:05.514791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:06.444778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:07.465957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:08.374488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:09.270258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:10.153466image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:11.172791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-30T14:15:12.110452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-30T14:15:16.464521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
danceabilityenergykeyloudnessspeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_msmode
danceability1.0000.1070.0290.1320.243-0.113-0.164-0.1080.478-0.088-0.0600.093
energy0.1071.0000.0330.7800.317-0.677-0.1060.1720.3250.2000.0090.080
key0.0290.0331.0000.0180.041-0.028-0.0030.0010.0410.0030.0060.250
loudness0.1320.7800.0181.0000.214-0.553-0.2980.1180.2420.161-0.0360.049
speechiness0.2430.3170.0410.2141.000-0.205-0.1740.1010.1700.083-0.0700.095
acousticness-0.113-0.677-0.028-0.553-0.2051.0000.019-0.084-0.125-0.190-0.0890.085
instrumentalness-0.164-0.106-0.003-0.298-0.1740.0191.000-0.085-0.234-0.0070.1430.053
liveness-0.1080.1720.0010.1180.101-0.084-0.0851.0000.0050.026-0.0310.028
valence0.4780.3250.0410.2420.170-0.125-0.2340.0051.0000.051-0.1900.027
tempo-0.0880.2000.0030.1610.083-0.190-0.0070.0260.0511.0000.0030.039
duration_ms-0.0600.0090.006-0.036-0.070-0.0890.143-0.031-0.1900.0031.0000.000
mode0.0930.0800.2500.0490.0950.0850.0530.0280.0270.0390.0001.000

Missing values

2023-04-30T14:15:13.337184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-30T14:15:13.574319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

danceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempouriduration_ms
00.9040.8134-7.10500.12100.031100.0069700.04710.810125.461spotify:track:0UaMYEvWZi0ZqiDOoHU3YI226864
10.7740.8385-3.91400.11400.024900.0250000.24200.924143.040spotify:track:6I9VzXrHxO9rA9A5euc8Ak198800
20.6640.7582-6.58300.21000.002380.0000000.05980.70199.259spotify:track:0WqIKmW4BTrj3eJFmnCKMv235933
30.8920.7144-6.05500.14100.201000.0002340.05210.817100.972spotify:track:1AWQoqb9bSvzTjaLralEkT267267
40.8530.6060-4.59610.07130.056100.0000000.31300.65494.759spotify:track:1lzr43nnXAijIGYnCT8M8H227600
50.8810.7882-4.66910.16800.021200.0000000.03770.592104.997spotify:track:0XUfyU2QviPAs6bxSpXYG4250373
60.6620.5075-8.23810.11800.257000.0000000.04650.67686.412spotify:track:68vgtRHr7iZHpzGpon6Jlo223440
70.5700.8212-4.38010.26700.178000.0000000.28900.408210.857spotify:track:3BxWKCI06eQ5Od8TY2JBeA225560
80.7130.6785-3.52500.10200.273000.0000000.14900.734138.009spotify:track:7H6ev70Weq6DdpZyyTmUXk271333
90.7270.9744-2.26100.06640.103000.0005320.17400.96579.526spotify:track:2PpruBYCo4H7WOBJ7Q2EwM235213
danceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempouriduration_ms
1700790.3580.9637-3.72510.10400.0136000.0003630.34700.105157.450spotify:track:0l6uRRIDPNw3OsmqCYkmvi328400
1700800.4430.9119-4.20800.08510.0124000.0000000.27800.680160.152spotify:track:642Zd1NC6fLzbLhjQ8ykcZ90375
1700810.5420.9812-2.96600.07790.0001290.0000300.14400.451139.958spotify:track:2aFVXUcOs57iVKYM0if4S4154286
1700820.7390.5213-5.47900.07070.1640000.0000890.43200.81299.993spotify:track:2AbwQmeiU43d4eIe2DkpYr207000
1700830.6850.53611-11.03600.11300.6780000.0000000.19300.75866.329spotify:track:5LnFmGMIGaIx4WoGFNWz33135609
1700840.5510.82211-7.01800.06140.0015400.0000000.21500.360127.043spotify:track:4cKPzfOUIZWXhu3GDnESjL265039
1700850.4800.7616-4.62810.03670.1200000.0000000.29600.359160.020spotify:track:0VICzaRdctD8XGfDstBjQY368767
1700860.4020.76711-6.71800.17100.0000770.0000030.39300.558178.293spotify:track:0B4YuevrDNhh71edZrFiOs179267
1700870.6780.8260-4.74200.05910.0052400.0000000.09510.706139.989spotify:track:7i9q0VPbENCoYnJUsJhX9d242347
1700880.6700.4839-7.12400.12500.2130000.0000000.05500.770170.115spotify:track:6uaJuze0LunPtcbCu1JNtq169390